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JANA2 is a multi-threaded event reconstruction framework being developed for Experimental Nuclear Physics. It is an LDRD1 funded project that will be the successor of the original JANA framework. JANA2 is a near complete rewrite emphasizing C++ language features that have only become available since the introduction of the C++11 standard. Features such as shared pointers, language native threading, and atomics are employed. This paper outlines the status of the project.

A newly completed (Oct. 2016) detector system of Extensive Air Showers (EAS) called Horizon-T (HT) is a part of Tien Shan high-altitude Science Station of Lebedev Physical Institute of the Russian Academy of Sciences, which is located 32 km from Almaty at the altitude of 3340 meters above the sea level. Horizon-T is constructed to study Extensive Air Showers in the energy range above ∼1016 eV coming from a wide range of zenith angles (0° - 85°). The system currently has eight working and two under construction charged particle detection points separated by the distance more than a kilometer. The ability to record each detector response with accuracy of 2 ns gives HT ability to study the temporary structure of EAS disk and apply the results to the event reconstruction. The reconstruction is therefore based on chronotron (< 0.5 ns), spatial and temporary distribution of charged particles within the detected EAS event. In this paper, we will show the simulated time distribution of charged particles in the EAS disk vs. distance from the axis and the correspondence to the data. A flow of the reconstruction of standard EAS events and the event display is presented as well as recent HT results.

Future long baseline neutrino experiments such as the DUNE experiment under construction at Fermilab will perform precision measurements of neutrino oscillations, including the potential for the discovery of CP violation in the lepton sector. These measurements require an understanding of the unoscillated neutrino beam with unprecedented accuracy. This will be provided by complex near detectors which consist of different subsystems including tracking elements and electromagnetic calorimetry. A high granularity in the calorimeter, provided by scintillator tiles with SiPM readout as used in the CALICE analog hadron calorimeter, provides the capability for direction reconstruction of photon showers, which can be used to determine the decay positions of neutral pions. This can enable the association of neutral pions to neutrino interactions in the tracker volume, improving the event reconstruction of the near detector. Beyond photon and electron reconstruction, the calorimeter also provides sensitivity to neutrons. In this presentation, we will discuss a simulation study exploring the potential of high granularity for the electromagnetic calorimeter of the DUNE near detector. Particular emphasis will be placed on the combination with a high pressure TPC as tracking detector, which puts particularly stringent requirements on the calorimeter. The dependence of the projected detector performance on granularity, absorber material and absorber thickness as well as geometric arrangement satisfying the constraints of the TPC are explored., 8 pages, 7 figures, proceedings of CALOR 2018, Eugene, OR, USA, May 2018

The ANDA experiment, which is currently under construction at the Facility for Antiproton and Ion Research (FAIR) in Darmstadt, Germany, will address fundamental questions in hadron and nuclear physics via interactions of antiprotons with nuclei. It will be installed at the High Energy Storage Ring (HESR), which will provide an antiproton beam with a momentum range of 1.5 - 15 GeV/c and a high average interaction rate on the fixed target of 2 × 107 events/s. The ANDA experiment will adopt a continuous data acquisition and the expected data rate transmitted to a high-bandwidth computing network will be in the order of 200 GB/s. However, in order to be able to select many different and rare physics processes, an indiscriminate hardware trigger would not suffice. Instead, an online software-based data selection system will be used to select only relevant data and thereby reduce the data rate by a factor of up to 1000. Due to the high interaction rate a highly advanced online analysis must be developed to deal also with overlapping events. Scalability and parallelization of the reconstruction algorithms are therefore a particular focus in the development process. A simulation framework called PandaRoot is used to further optimize the detector performance and develop and evaluate different reconstruction algorithms for event building, tracking and particle identification. An overview about PandaRoot and the requirements on the event reconstruction algorithms is presented and algorithms for the event time reconstruction currently under development are discussed.

One of the main purposes of the physics program of the future heavy ion experiment CBM (FAIR, Germany) is to understand the properties of strongly interacting matter at very high baryonic densities and to study the possibility of a phase transition to a deconfined and chirally restored phase of quark matter. The experiment will operate at high interaction rates up to 10 MHz, that requires a full event reconstruction in real time. In order to make an efficient event selection online a clean sample of particles has to be provided by the reconstruction package called First Level Event Selection (FLES). The FLES package operates in two stages. First, particles registered in the CBM detector system are reconstructed. Then short-lived particles decayed before or inside the setup are searched based on their charged and neutral daughter particles. Since the FLES package is developed to run on many-core computer architectures, the reconstruction of particles is done in parallel that provides a possibility for a global competition between particle candidates. Such a global event topology reconstruction significantly improves suppression of a combinatorial background and provides for further physics analysis a very clean sample of particles produced at different stages of heavy ion collision. The global event topology reconstruction procedure and the results of its application to simulated collisions in the CBM detector setup are presented and discussed.

The high luminosity upgrade of the Large Hadron Collider (HL-LHC) at CERN is expected to provide instantaneous luminosities of 5 × 10^(34) cm^(−2) s^(−1). The high luminosities expected at the HL-LHC will be accompanied by a factor of 5 to 10 more pileup compared with LHC conditions in 2015, causing general confusion for particle identification and event reconstruction. Precision timing allows to extend calorimetric measurements into such a high density environment by subtracting the energy deposits from pileup interactions. Calorimeters employing silicon as the active component have recently become a popular choice for the HL- LHC and future collider experiments which face very high radiation environments. We present studies of basic calorimetric and precision timing measurements using a prototype composed of tungsten absorber and silicon sensor as the active medium. We show that for the bulk of electromagnetic showers induced by electrons in the range of 20 GeV to 30 GeV, we can achieve time resolutions better than 25 ps per single pad sensor.

The Compressed Baryonic Matter experiment (CBM) is a next-generation heavy-ion experiment to be operated at the FAIR facility, currently under construction in Darmstadt, Germany. A key feature of CBM is very high interaction rate, exceeding those of contemporary nuclear collision experiments by several orders of magnitude. Such interaction rates forbid a conventional, hardware-triggered readout; instead, experiment data will be freely streaming from self-triggered front-end electronics. In order to reduce the huge raw data volume to a recordable rate, data will be selected exclusively on CPU, which necessitates partial event reconstruction in real-time. Consequently, the traditional segregation of online and offline software vanishes; an integrated on- and offline data processing concept is called for. In this paper, we will report on concepts and developments for computing for CBM as well as on the status of preparations for its first physics run.

$\bar{{\rm{P}}}$ANDA is a future hadron and nuclear physics experiment at the FAIR facility in construction in Darmstadt, Germany. Unlike the majority of current experiments, $\bar{{\rm{P}}}$ANDA's strategy for data acquisition is based on online event reconstruction from free-streaming data, performed in real time entirely by software algorithms using global detector information. This paper reports on the status of the development of algorithms for the reconstruction of charged particle tracks, targeted towards online data processing applications, designed for execution on data-parallel processors such as GPUs (Graphic Processing Units). Two parallel algorithms for track finding, derived from the Circle Hough algorithm, are being developed to extend the parallelism to all stages of the algorithm. The concepts of the algorithms are described, along with preliminary results and considerations about their implementations and performance

The LHC is the world’s most powerful particle accelerator, colliding protons at centre of mass energy of 13 TeV. As the energy and frequency of collisions has grown in the search for new physics, so too has demand for computing resources needed for event reconstruction. We will report on the evolution of resource usage in terms of CPU and RAM in key ATLAS offline reconstruction workflows at the TierO at CERN and on the WLCG. Monitoring of workflows is achieved using the ATLAS PerfMon package, which is the standard ATLAS performance monitoring system running inside Athena jobs. Systematic daily monitoring has recently been expanded to include all workflows beginning at Monte Carlo generation through to end-user physics analysis, beyond that of event reconstruction. Moreover, the move to a multiprocessor mode in production jobs has facilitated the use of tools, such as “MemoryMonitor”, to measure the memory shared across processors in jobs. Resource consumption is broken down into software domains and displayed in plots generated using Python visualization libraries and collected into pre-formatted auto-generated Web pages, which allow the ATLAS developer community to track the performance of their algorithms. This information is however preferentially filtered to domain leaders and developers through the use of JIRA and via reports given at ATLAS software meetings. Finally, we take a glimpse of the future by reporting on the expected CPU and RAM usage in benchmark workflows associated with the High Luminosity LHC and anticipate the ways performance monitoring will evolve to understand and benchmark future workflows.

The SuperNEMO experiment will search for neutrinoless double-beta decay in the Modane Underground Laboratory. This decay mode, if observed, confirms that neutrinos are Majorana fermions. It would be a new lepton violating process, and would provide a measurement of the absolute neutrino mass. The SuperNEMO experiment is designed to reach a half-life sensitivity of 1026 years corresponding to an effective Majorana neutrino mass of 50−100 meV. The SuperNEMO demonstrator module is the first stage of the experiment, containing 7kg of 82Se, with an expected sensitivity of T½ (0ν) > 6.5×1024 y after 2.5 years. Full topological event reconstruction is achieved through the use of a wire tracker operating in Geiger mode combined with scintillator calorimeter modules. Construction of the demonstrator module is well underway. We present the design of the tracker, and the current status of the construction and commissioning efforts.

A new event reconstruction algorithm, fiTQun, has been developed for the Super-Kamiokande detector. Super-Kamiokande is a ring-imaging water Cherenkov detector with a 22.5-kton fiducial volume located 1000 m underground in the Kamioka Mine in Japan. Neutrino events in the detector’s central volume produce charged particles whose Cherenkov rings are imaged by more than 11,000 photomultiplier tubes (PMTs) that line the walls of the detector. This new reconstruction software is able to reconstruct the detailed kinematics of the neutrino interaction from the charge and timing information of each PMT. In contrast to previous reconstruction algorithms that use image processing and pattern recognition techniques, fiTQun uses a maximum-likelihood approach that takes advantage of the known Cherenkov emission profiles and the detector response to evaluate the likelihood of a given reconstruction hypothesis. This approach provides a unifying framework for all aspects of the event reconstruction, including kinematics, ring counting, and particle identification. Using fiTQun to reconstruct neutrino events for the Tokai-to-Kamioka (T2K) experiment can greatly improve the current event selection by reducing pion backgrounds, improving separation of electrons and muons, and reconstructing the neutrino energy with greater precision. These improvements should significantly increase T2K’s sensitivity to the oscillation parameters.

SuperNEMO, successor to the NEMO3 experiment, is looking for the neutrinoless double beta decay. Its unique design, combining both tracking and calorimetry techniques, provides essential topological informations. Indeed, fully reconstructing the event kinematics : allows a powerful background discrimination, would discriminate between the several hypothesized underlying mechanisms, but also gives access to a variety of event topologies which can be used to measure the different background contributions. The SuperNEMO software relies on a range of algorithms to ensure a faithful event reconstruction. The improved detector performance for $\gamma$ detection coupled to new $\gamma$-reconstruction algorithms, based on geometrical and Time-of-Flight criteria, will not only improve the measurements of the $\gamma$-emitting backgrounds ($^{208}$Tl, $^{214}$Bi...) but also increase the sensitivity for the search of $\beta\beta$-decays to the excited states.

Neutrinoless double-beta decay is a uniquely sensitive probe of lepton number violating processes, and its observation may answer fundamental questions in neutrino physics, including the nature and the mass scale of the light neutrinos. Tracking calorimeter experiments have particular strengths, including the ability to search for neutrinoless double-beta decay amongst several different isotopes hosted in source foils. Full event reconstruction provides powerful background rejection capability, and the ability to disentangle different mechanisms for neutrinoless double-beta decay in the event of its discovery. These proceedings will give the latest results from the NEMO-3 experiment, including new double-beta decay measurements using the isotopes 48Ca and 150Nd. The current status and future prospects for the SuperNEMO experiment will also be presented.

In this paper we present SOA based CLAS12 event Reconstruction and Analyses (CLARA) framework. CLARA design focus is on two main traits: real-time data stream processing, and service-oriented architecture (SOA) in a flow based programming (FBP) paradigm. Data driven and data centric architecture of CLARA presents an environment for developing agile, elastic, multilingual data processing applications. The CLARA framework presents solutions capable of processing large volumes of data interactively and substantially faster than batch systems.

Non-normally distributed data are ubiquitous in many areas of science, including high-energy physics. We present a general formalism for constrained fits, also called data reconciliation, with data that are not normally distributed. It is based on Bayesian reasoning and implemented via MCMC sampling. We show how systems of both linear and non-linear constraints can be efficiently treated. We also show how the fit can be made robust against outlying observations. The method is demonstrated on a couple of examples ranging from material flow analysis to the combination of non-normal measurements. Finally, we discuss possible applications in the field of event reconstruction, such as vertex fitting and kinematic fitting with non-normal track errors.

Future experiments such as the Deep Underground Neutrino Experiment (DUNE) will use very large Liquid Argon Projection Chambers (LArTPC) containing tens of kilotons of cryogenic medium. To be able to utilize sensitive volume of that size, current design employs arrays of wire electrodes grouped in readout planes, arranged with a stereo angle. This leads to certain challenges for object reconstruction due to ambiguities inherent in such a scheme. We present a novel reconstruction method (named "Wirecell") inspired by principles used in tomography, which brings the LArTPC technology closer to its full potential.

The gamma-camera is the detector for nuclear medical imaging where the photomultiplier tubes (PMTs) could be replaced by the silicon photomultipliers (SiPMs). Common systems have the energy resolution about 10% and intrinsic spatial resolution about 3 mm (FWHM). In order to achieve the requirement energy and spatial resolution the classical Anger's logic should be modified. In case of a standard monolithic thallium activated sodium iodide scintillator (500x400x10 mm3) and SiPM readout it could be done with identification of the clusters. We show that this approach has a good results with the simulated data.

The NOvA experiment is a long-baseline neutrino oscillation experiment utilizing the NuMI beam generated at Fermilab. The experiment will measure the oscillations within a muon neutrino beam in a 300 ton Near Detector located underground at Fermilab and a functionally-identical 14 kiloton Far Detector placed 810 km away. The detectors are liquid scintillator tracking calorimeters with a fine-grained cellular structure that provides a wealth of information for separating the different particle track and shower topologies. Each detector has its own challenges with the Near Detector seeing multiple overlapping neutrino interactions in each event and the Far Detector having a large background of cosmic rays due to being located on the surface. A series of pattern recognition techniques have been developed to go from event records, to spatially and temporally separating individual interactions, to vertexing and tracking, and particle identification. This combination of methods to achieve the full event reconstruction will be discussed.

P̅ANDA is a future hadron and nuclear physics experiment at the FAIR facility in construction in Darmstadt, Germany. In contrast to the majority of current experiments, PANDA's strategy for data acquisition is based on event reconstruction from free-streaming data, performed in real time entirely by software algorithms using global detector information. This paper reports the status of the development of algorithms for the reconstruction of charged particle tracks, optimized online data processing applications, using General-Purpose Graphic Processing Units (GPU). Two algorithms for trackfinding, the Triplet Finder and the Circle Hough, are described, and details of their GPU implementations are highlighted. Average track reconstruction times of less than 100 ns are obtained running the Triplet Finder on state-of- the-art GPU cards. In addition, a proof-of-concept system for the dispatch of data to tracking algorithms using Message Queues is presented.

The future heavy-ion experiment CBM (FAIR/GSI, Darmstadt, Germany) will focus on the measurement of very rare probes at interaction rates up to 10 MHz with data flow of up to 1 TB/s. The beam will provide free stream of beam particles without bunch structure. That requires full online event reconstruction and selection not only in space, but also in time, so-called 4D event building and selection. This is a task of the First-Level Event Selection (FLES).

Tunka-133 is an air-Cherenkov array placed in Siberia, near the southern tip of Lake Baikal, which registers air showers induced by cosmic rays from initial particles with energies of 10$^{16}$-10$^{18}$ eV. After several years of successful data collection, this array was extended by other detector arrays. One of them is the Tunka Radio Extension (Tunka-Rex): the radio array consists of presently 25 antenna stations connected to the data acquisition of Tunka-133. This combination provides hybrid measurements and the possibility for cross-calibration between the air-Cherenkov and radio measurement techniques. The main goal of Tunka-Rex is to determine the precision of the reconstruction of air-shower parameters using the radio detection technique. We present the latest results on the event reconstruction.

The PANDA experiment at the new FAIR facility will cover a broad experimental programme in hadron structure and spectroscopy. As a multipurpose detector, the PANDA spectrometer needs to ensure almost 4π coverage of the scattering solid angle, full and accurate multiple-particle event reconstruction and very good particle identification capabilities. The electromagnetic calorimeter (EMC) will be a key item for many of these aspects. Particle energies ranging from some MeVs to several GeVs have to be measured with a relative resolution of 1% ⊕ 2%/√E/GeV . It will be a homogeneous calorimeter made of PbWO4 crystals and will be operated at -25°C, in order to improve the scintillation light yield. With the exception of the very forward section, the light will be detected by large area avalanche photodiodes (APDs). The current pulses from the APDs will be integrated, amplified and shaped by ASIC chips which were developed for this purpose. The whole calorimeter has been designed in three sections: a forward end-cap, a central barrel and a backward end-cap (BWEC). In this contribution, a status report on the development of the BWEC is presented.

Development of fast and efficient event reconstruction algorithms is an important and challenging task in the Compressed Baryonic Matter (CBM) experiment at the future FAIR facility. The event reconstruction algorithms have to process terabytes of input data produced in particle collisions. In this contribution, several event reconstruction algorithms are presented. Optimization of the algorithms in the following CBM detectors are discussed: Ring Imaging Cherenkov (RICH) detector, Transition Radiation Detectors (TRD) and Muon Chamber (MUCH). The ring reconstruction algorithm in the RICH is discussed. In TRD and MUCH track reconstruction algorithms are based on track following and Kalman Filter methods. All algorithms were significantly optimized to achieve maximum speed up and minimum memory consumption. Obtained results showed that a significant speed up factor for all algorithms was achieved and the reconstruction efficiency stays at high level.

The R3B experiment (Reaction studies with Relativistic Radioactive Beams) will be built within the future FAIR / GSI (Facility for Antiproton and Ion Research) in Darmstadt, Germany. The international collaboration R3B has a scientific program devoted to the physics of stable and radioactive beams at energies between 150 MeV and 1.5 GeV per nucleon. In preparation for the experiment, the R3BRoot software framework is under development, it deliver detector simulation, reconstruction and data analysis. The basic functionalities of the framework are handled by the FairRoot framework which is used also by the other FAIR experiments (CBM, PANDA, ASYEOS, etc) while the R3B detector specifics and reconstruction code are implemented inside R3BRoot. In this contribution first results of data analysis from the detector prototype test in November 2012 will be reported, moreover, comparison of the tracker performance versus experimental data, will be presented.

Several scenarios, both present and future, require re-simulation of the trigger response in the ATLAS experiment at the LHC. While software for the detector response simulation and event reconstruction is allowed to change and improve, the trigger response simulation has to reflect the conditions at which data was taken. This poses a maintenance and data preservation problem. Several strategies have been considered and a proof-of-concept model using virtualization has been developed. While the virtualization with CernVM elegantly solves several aspects of the data preservation problem, the limitations of current methods for contextualization of the virtual machine as well as incompatibilities in the currently used data format introduces new challenges. In this proceeding these challenges, their current solutions and the proof of concept model for precise trigger simulation are discussed.

MINERνA (Main INjector ExpeRiment ν-A) is a few-GeV neutrino scattering experiment that began taking data in the NuMI beam at Fermilab (FNAL) in the Fall of 2009. MINERνA employs a fine-grained detector, with an eight ton active target region composed of plastic scintillator. It also uses nuclear targets composed of carbon, iron, and lead placed upstream of the active region to measure ν-A dependence. The experiment will provide important inputs for neutrino oscillation experiments and a pure weak probe of nuclear structure. We offer a set of initial kinematic distributions of interest and provide a summary of current operations and event reconstruction status.

A next generation B-factory experiment, Belle II, is now being constructed at KEK in Japan. The upgraded accelerator SuperKEKB is designed to have the maximum luminosity of 8 × 1035 cm−2s−1 that is a factor 40 higher than the current world record. As a consequence, the Belle II detector yields a data stream of the event size ~1 MB at a Level 1 rate of 30 kHz. The Belle II High Level Trigger (HLT) is designed to reduce the Level 1 rate to 1/5 by performing the real time full event reconstruction and by applying the physics level event selection as the software trigger. In this paper, the development of the high level trigger system for Belle II and its performance is discussed.

Chip-Multiprocessors are going to support massive parallelism by many additional physical and logical cores. Improving performance can no longer be obtained by increasing clock-frequency because the technical limits are almost reached. Instead, parallel execution must be used to gain performance. Resources like main memory, the cache hierarchy, bandwidth of the memory bus or links between cores and sockets are not going to be improved as fast. Hence, parallelism can only result into performance gains if the memory usage is optimized and the communication between threads is minimized. Besides concurrent programming has become a domain for experts. Implementing multi-threading is error prone and labor-intensive. A full reimplementation of the whole AliRoot source-code is unaffordable. This paper describes the effort to evaluate the adaption of AliRoot to the needs of multi-threading and to provide the capability of parallel processing by using a semi-automatic source-to-source transformation to address the problems as described before and to provide a straight-forward way of parallelization with almost no interference between threads. This makes the approach simple and reduces the required manual changes in the code. In a first step, unconditional thread-safety will be introduced to bring the original sequential and thread unaware source-code into the position of utilizing multi-threading. Afterwards further investigations have to be performed to point out candidates of classes that are useful to share amongst threads. Then in a second step, the transformation has to change the code to share these classes and finally to verify if there are anymore invalid interferences between threads.

The ALICE HLT is a dedicated real-time system for online event reconstruction and triggering. Its main goal is to reduce the raw data volume read from the detectors by an order of magnitude, to fit within the available data acquisition bandwidth. This is accomplished by a combination of data compression and triggering. When HLT is enabled, data is recorded only for events selected by HLT. The combination of both approaches allows for flexible data reduction strategies. Event reconstruction places a high computational load on HLT. Thus, a large dedicated computing cluster is required, comprising 248 machines, all interconnected with InfiniBand. Running a large system like HLT in production mode proves to be a challenge. During the 2010 pp and Pb-Pb data-taking period, many problems were experienced that led to a sub-optimal operational efficiency. Lessons were learned and certain crucial changes were made to the architecture and software in preparation for the 2011 Pb-Pb run, in which HLT had a vital role performing data compression for ALICE's largest detector, the TPC. An overview of the status of the HLT and experience from the 2010/2011 production runs are presented. Emphasis is given to the overall performance, showing an improved efficiency and stability in 2011 compared to 2010, attributed to the significant improvements made to the system. Further opportunities for improvement are identified and discussed.

High resolution detectors in high energy nuclear physics deliver a huge amount of data which is often a challenge for the data acquisition and mass storage. Lossless compression techniques on the level of the raw data can provide compression ratios up to a factor of two. Higher compression ratios can be reached by introducing an appropriate model for the raw data and storing relevant information for the event reconstruction with respect to that model. In ALICE, a data compression technique has been developed for the Time Projection Chamber (TPC) to reach an overall compression factor suited for data taking in Heavy Ion collisions. The ALICE High Level Trigger provides online calculation of the TPC clusters from the raw data, followed by tracking, thus producing a fully reconstructed event. Storing the reconstructed cluster data in an appropriate compressed format for utilization in the off-line reconstruction allows to discard the original raw data of the TPC. In the presented solution, compression factors of four to six are achieved without significantly affecting the physics performance. By associating space points to reconstructed tracks, all relevant parameters can be further transformed into a format suitable for Huffman compression. In a first conservative approach, all reconstructed clusters are kept in the data. Data compression has been implemented for the ALICE TPC in 2011 for usage in the Heavy Ion data-taking. In this contribution the results on 2011 real data are presented for the first time.

The ALICE High-Level Trigger (HLT) has a large high-performance computing cluster at CERN whose main objective is to perform real-time analysis on the data generated by the ALICE experiment and scale it down to at-most 4GB/sec - which is the current maximum mass-storage bandwidth available. Data-flow in this cluster is controlled by a custom designed software framework. It consists of a set of components which can communicate with each other via a common control interface. The software framework also supports the creation of different configurations based on the detectors participating in the HLT. These configurations define a logical data processing "chain" of detector data-analysis components. Data flows through this software chain in a pipelined fashion so that several events can be processed at the same time. An instance of such a chain can run and manage a few thousand physics analysis and data-flow components. The HLT software and the configuration scheme used in the 2011 heavy-ion runs of ALICE, has been discussed in this contribution.

The conversion of photons into electron-positron pairs in the detector material is a nuisance in the event reconstruction of high energy physics experiments, since the measurement of the electromagnetic component of interaction products results degraded. Nonetheless this unavoidable detector effect can be also extremely useful. The reconstruction of photon conversions can be used to probe the detector material and to accurately measure soft photons that come from radiative decays in heavy flavor physics. In fact a converted photon can be measured with very high momentum resolution by exploiting the excellent reconstruction of charged tracks of a tracking detector as the one of CMS at LHC. The main issue is that photon conversion tracks are difficult to reconstruct for standard reconstruction algorithms. They are typically soft and very displaced from primary interaction vertex. An innovative seeding technique that exploits the peculiar photon conversion topology, successfully applied in the CMS track reconstruction sequence, is presented. The performances of this technique and the substantial enhancement of photon conversion reconstruction efficiency are discussed. Application examples are given.

The estimation of the compatibility of large amounts of histogram pairs is a recurrent problem in high energy physics. The issue is common to several different areas, from software quality monitoring to data certification, preservation and analysis. Given two sets of histograms, it is very important to be able to scrutinize the outcome of several goodness of fit tests, obtain a clear answer about the overall compatibility, easily spot the single anomalies and directly access the concerned histogram pairs. This procedure must be automated in order to reduce the human workload, therefore improving the process of identification of differences which is usually carried out by a trained human mind. Some solutions to this problem have been proposed, but they are experiment specific. RelMon depends only on ROOT and offers several goodness of fit tests (e.g. chi-squared or Kolmogorov-Smirnov). It produces highly readable web reports, in which aggregations of the comparisons rankings are available as well as all the plots of the single histogram overlays. The comparison procedure is fully automatic and scales smoothly towards ensembles of millions of histograms. Examples of RelMon utilisation within the regular workflows of the CMS collaboration and the advantages therewith obtained are described. Its interplay with the data quality monitoring infrastructure is illustrated as well as its role in the QA of the event reconstruction code, its integration in the CMS software release cycle process, CMS user data analysis and dataset validation.

ALICE is a dedicated heavy ion experiment at the CERN LHC. The ALICE High Level Trigger was designed to select events with desirable physics properties. Data from several of the major subdetectors in ALICE are processed by the HLT for real-time event reconstruction, for instance the Inner Tracking System, the Time Projection Chamber, the electromagnetc calorimeters, the Transition Radiation Detector and the muon spectrometer. The HLT reconstructs events in real-time and thus provides input for trigger algorithms. It is necessary to monitor the quality of the reconstruction where one focuses on track and event properties. Also, HLT implemented data compression for the TPC during the heavy ion data taking in 2011 to reduce the data rate from the ALICE detector. The key for the data compression is to store clusters (spacepoints) calculated by HLT rather than storing raw data. It is thus very important to monitor the cluster finder performance as a way to monitor the data compression. The data monitoring is divided into two stages. The first stage is performed during data taking. A part of the HLT production chain is dedicated to performing online monitoring and facilities are available in the HLT production cluster to have real-time access to the reconstructed events in the ALICE control room. This includes track and event properties, and in addition, this facility gives a way to display a small fraction of the reconstructed events in an online display. The second part of the monitoring is performed after the data has been transferred to permanent storage. After a post-process of the real-time reconstructed data, one can look in more detail at the cluster finder performance, the quality of the reconstruction of tracks, vertices and vertex position. The monitoring solution is presented in detail, with special attention to the heavy ion data taking of 2011.

The Daya Bay Reactor Neutrino Experiment utilizes an RPC detector system to detect cosmic-ray muons for offline suppression of muon-induced backgrounds. This proceeding paper introduces the structure of the offline software of the RPC detector system, including simulation, detector calibration, and event reconstruction. In addition, preliminary analysis results based on the offline software are reported.

Maximizing the physics performance of detectors being designed for the International Linear Collider, while remaining sensitive to cost constraints, requires a powerful, efficient, and flexible simulation, reconstruction and analysis environment to study the capabilities of a large number of different detector designs. The preparation of Letters Of Intent for the International Linear Collider involved the detailed study of dozens of detector options, layouts and readout technologies; the final physics benchmarking studies required the reconstruction and analysis of hundreds of millions of events. We describe the Java-based software toolkit (org.lcsim) which was used for full event reconstruction and analysis. The components are fully modular and are available for tasks from digitization of tracking detector signals through to cluster finding, pattern recognition, track-fitting, calorimeter clustering, individual particle reconstruction, jet-finding, and analysis. The detector is defined by the same xml input files used for the detector response simulation, ensuring the simulation and reconstruction geometries are always commensurate by construction. We discuss the architecture as well as the performance.

The silicon strip tracker of CMS is by far the biggest detector of its kind ever operated. Its 15 000 detector modules and 9 million readout channels are individually calibrated in order to achieve the optimal data quality for the experiment. Software tools were designed to automate the operations and reduce the need for maintenance as much as possible, taking care to use pre-existing software frameworks, when possible. The calibration software implements a dedicated scheme of event building, on-line distributed analysis, storage management, data analysis and configuration archival. Dedicated user interfaces and web-applications were developed to ease the operations, speed-up the calibration process, monitor its quality, and track the problems. A complete set of monitoring analyses are also performed online during data taking to validate the data acquired. The calibration parameters are measured continuously, and the values are fed back to the calibration database in order to refine the on-line event reconstruction. A review of the software tools and calibration processes is given here and the obtained results are discussed.

We report on the status and plans for the event reconstruction software of the CMS experiment. The CMS reconstruction algorithms are the basis for a wide range of data analysis approaches currently under study by the CMS collaboration using the first high-energy run of the LHC. These algorithms have been primarily developed and validated using simulated data samples, and are now being commissioned with LHC proton-proton collision data samples. The CMS reconstruction is now operated routinely on all events triggered by the CMS detector, both in a close to real-time prompt reconstruction processing and in frequent passes over the full recorded CMS data set. We discuss the overall software design, development cycle, computational requirements and performance, recent operational performance, and planned improvements of the CMS reconstruction software.

The High Level Trigger for the ALICE experiment at LHC is a powerful, sophisticated tool aimed at compressing the raw data volume and issuing selective triggers for events with desirable physics content. At its current state it integrates information from all major ALICE detectors, i. e. the inner tracking system, the time projection chamber, the electromagnetic calorimeters, the transition radiation detector and the muon spectrometer performing real-time event reconstruction. The steam engine behind HLT is a high performance computing cluster of several hundred nodes. It has to reduce the data rate from 25 GB/s to 1.25 GB/s for fitting the DAQ mass storage bandwidth. The cluster is served by a full GigaBit Ethernet network, in addition to an InfiniBand backbone network. To cope with the great challenge of Pb+Pb collisions in autumn 2010, its performance capabilities are being enhanced with the addition of new nodes. Towards the same end the first GPU co-processors are in place. During the first period of data taking with p+p collisions the HLT was extensively used to reconstruct, analyze and display data from the various participating detectors. Among other tasks it contributed to the monitoring of the detector performance, selected events for their calibration and efficiency studies, and estimated primary and secondary vertices from p+p collisions identifying V0 topologies. The experience gained during these first months of online operation will be presented.

Although Moore's Law remains technically valid, the performance enhancements in computing which traditionally resulted from increased CPU speeds ended years ago. Chip manufacturers have chosen to increase the number of core CPUs per chip instead of increasing clock speed. Unfortunately, these extra CPUs do not automatically result in improvements in simulation or reconstruction times. To take advantage of this extra computing power requires changing how software is written. Event reconstruction is globally serial, in the sense that raw data has to be unpacked first, channels have to be clustered to produce hits before those hits are identified as belonging to a track or shower, tracks have to be found and fit before they are vertexed, etc. However, many of the individual procedures along the reconstruction chain are intrinsically independent and are perfect candidates for optimization using multi-core architecture. Threading is perhaps the simplest approach to parallelizing a program and Java includes a powerful threading facility built into the language. We have developed a fast and flexible reconstruction package (org.lcsim) written in Java that has been used for numerous physics and detector optimization studies. In this paper we present the results of our studies on optimizing the performance of this toolkit using multiple threads on many-core architectures.

We present some preliminary findings and results from activities in Europe and the USA working towards an automated, algorithmic, reconstruction of particle interactions in liquid argon time projection chambers.

FairRoot is the simulation and analysis framework used by CBM and PANDA experiments at FAIR/GSI. The use of graphics processor units (GPUs) for event reconstruction in FairRoot will be presented. The fact that CUDA (Nvidia's Compute Unified Device Architecture) development tools work alongside the conventional C/C++ compiler, makes it possible to mix GPU code with general-purpose code for the host CPU, based on this some of the reconstruction tasks can be send to the graphic cards. Moreover, tasks that run on the GPU's can also run in emulation mode on the host CPU, which has the advantage that the same code is used on both CPU and GPU.

The ATLAS experiment at the Large Hadron Collider reads out 100 Million electronic channels at a rate of 200 Hz. Before the data are shipped to storage and analysis centres across the world, they have to be checked to be free from irregularities which render them scientifically useless. Data quality offline monitoring provides prompt feedback from full first-pass event reconstruction at the Tier-0 computing centre and can unveil problems in the detector hardware and in the data processing chain. Detector information and reconstructed proton-proton collision event characteristics are distilled into a few key histograms and numbers which are automatically compared with a reference. The results of the comparisons are saved as status flags in a database and are published together with the histograms on a web server. They are inspected by a 24/7 shift crew who can notify on-call experts in case of problems and in extreme cases signal data taking abort.

The LHCb experiment at the LHC accelerator at CERN will collide particle bunches at 40 MHz. After a first level of hardware trigger with output at 1 MHz, the physically interesting collisions will be selected by running dedicated trigger algorithms, the High Level Trigger (HLT), in the Online computing farm. This farm consists of 16000 CPU cores and 40 TB of storage space. Although limited by environmental constraints, the computing power is equivalent to that provided by all Tier-1's to LHCb. The HLT duty cycle follows the LHC collisions, thus it has several months of winter shutdown, as well as several shorter machine and experiment downtime periods. This work describes the strategy for using these idle resources for event reconstruction. Due to the specific features of the Online Farm, typical processing a la Tier-1 (1 file per core) is not feasible. A radically different approach has been chosen, based on parallel processing the data in farm slices of (1000) cores. Single events are read from the input files, distributed to the cluster and merged back into files once they have been processed. A detailed description of this architectural solution, the obtained performance and how it will be connected to the LHCb production system will be described.

The C++ reconstruction framework JANA has been written to support the next generation of Nuclear Physics experiments at Jefferson Lab in anticipation of the 12GeV upgrade. The JANA framework was designed to allow multi-threaded event processing with a minimal impact on developers of reconstruction software. As we enter the multi-core (and soon many-core) era, thread-enabled code will become essential to utilizing the full processor power available without invoking the logistical overhead of managing many individual processes. Event-based reconstruction lends itself naturally to multi-threaded processing. Emphasis will be placed on the multi-threading features of the framework. Test results of the scaling of event processing rates with number of threads are presented.

Assessing the quality of data recorded with the ATLAS detector is crucial for commissioning and operating the detector to achieve sound physics measurements. In particular, the fast assessment of complex quantities obtained during event reconstruction and the ability to easily track them over time are especially important given the large data throughput and the distributed nature of the analysis environment. The data are processed once on a computer farm comprising O(1, 000) nodes before being distributed on the Grid, and reliable, centralized methods must be used to organize, merge, present, and archive data-quality metrics for performance experts and analysts. A review of the tools and approaches employed by the detector and physics groups in this environment and a summary of their performances during commissioning are presented.

The experiments design studies at FAIR are done using a ROOT based simulation and analysis framework: FairRoot. The framework is using the Virtual Monte Carlo concept which allows to perform simulation using Geant3, Geant4 or Fluka without changing the user code. The same framework is then used for event reconstruction. An Oracle database with a build-in versioning management is used to efficiently store the detector geometry, materials and parameters. A generic track follower based on Geane has been implemented which allows precise and fast tracking algorithm development.

22nd International Conference on Computing in High Energy and Nuclear Physics , CHEP2016, San Francisco, USA, 10 Oct 2016 - 14 Oct 2016; Journal of physics / Conference Series 898, 042017 (2017). doi:10.1088/1742-6596/898/4/042017, The DD4HEP detector description toolkit offers a flexible and easy-to-use solution for the consistent and complete description of particle physics detectors in a single system. The sub-component DDREC provides a dedicated interface to the detector geometry as needed for event reconstruction. With DDREC there is no need to define an additional, separate reconstruction geometry as is often done in HEP, but one can transparently extend the existing detailed simulation model to be also used for the reconstruction. Based on the extension mechanism of DD4HEP, DDREC allows one to attach user defined data structures to detector elements at all levels of the geometry hierarchy. These data structures define a high level view onto the detectors describing their physical properties, such as measurement layers, point resolutions, and cell sizes. For the purpose of charged particle track reconstruction, dedicated surface objects can be attached to every volume in the detector geometry. These surfaces provide the measurement directions, local-to-global coordinate transformations, and material properties. The material properties, essential for the correct treatment of multiple scattering and energy loss effects in charged particle reconstruction, are automatically averaged from the detailed geometry model along the normal of the surface. Additionally, a generic interface allows the user to query material properties at any given point or between any two points in the detector's world volume. In this paper we will present DDREC and how it is used together with the linear collider tracking software and the particle-flow package PANDORAPFA for full event reconstruction of the ILC detector concepts ILD and SiD, and of CLICdp. This flexible tool chain is also well suited for other future accelerator projects such as FCC and CEPC., Published by IOP Publ., Bristol